--- name: batch-processor-8e0960d18c9b description: >- Process multiple documents in bulk with parallel execution. Use when a user asks to batch process files, convert many documents at once, run parallel file operations, bulk rename, bulk transform, or process a directory of files concurrently. Covers parallel execution, error handling, and progress tracking. license: Apache-2.0 compatibility: "bash with GNU xargs; GNU parallel, Python 3.9+, ImageMagick 7, pandoc, tesseract optional per task" metadata: author: terminal-skills version: "1.1.0" category: automation tags: "batch, parallel, bulk, processing, automation" use-cases: "Convert hundreds of files between formats in parallel, Apply transformations to every file in a directory, Run bulk data extraction with progress tracking" agents: "claude-code, openai-codex, gemini-cli, cursor" --- # Batch Processor ## Overview Process multiple documents and files in bulk using parallel execution. Handles large-scale file operations including format conversion, data extraction, transformation, and validation across hundreds or thousands of files with configurable concurrency, error recovery, and progress reporting. ## Instructions When a user asks for batch processing, determine which approach fits their needs: ### Task A: Parallel file processing with shell tools For simple transformations, use `xargs` or GNU `parallel`: ```bash # Convert all PNG files to JPEG using ImageMagick 7 (8 parallel jobs). # -print0 / -0 keeps file names with spaces intact. On ImageMagick 6 use `convert` instead of `magick`. find ./images -name "*.png" -print0 | xargs -0 -P 8 -I {} bash -c \ 'magick "$1" "${1%.png}.jpg"' _ {} # Process files with GNU parallel (a separate install) and a progress bar find ./docs -name "*.csv" -print0 | parallel -0 --bar --jobs 8 \ 'python transform.py {} {.}_processed.csv' # Bulk compress PDFs into a separate folder (4 parallel jobs); originals stay untouched mkdir -p ./reports-small find ./reports -name "*.pdf" -print0 | xargs -0 -P 4 -I {} bash -c \ 'gs -sDEVICE=pdfwrite -dPDFSETTINGS=/ebook -dNOPAUSE -dBATCH -dQUIET \ -sOutputFile="./reports-small/$(basename "$1")" "$1"' _ {} ``` `xargs -P` is available in GNU and BSD xargs; `parallel` adds `--bar`, `--joblog` and `--resume-failed`. ### Task B: Python batch processor with concurrency control Create a reusable batch processing script: ```python import asyncio import os from pathlib import Path from dataclasses import dataclass, field @dataclass class BatchResult: total: int = 0 success: int = 0 failed: int = 0 errors: list = field(default_factory=list) async def process_file(filepath: Path, semaphore: asyncio.Semaphore) -> tuple[bool, str]: async with semaphore: try: # Replace with actual processing logic content = filepath.read_text() output = content.upper() # Example transformation out_path = filepath.with_suffix('.processed' + filepath.suffix) out_path.write_text(output) return True, str(filepath) except Exception as e: return False, f"{filepath}: {e}" async def batch_process( input_dir: str, pattern: str = "*.*", max_concurrent: int = 10 ) -> BatchResult: semaphore = asyncio.Semaphore(max_concurrent) files = list(Path(input_dir).glob(pattern)) result = BatchResult(total=len(files)) tasks = [process_file(f, semaphore) for f in files] for coro in asyncio.as_completed(tasks): success, msg = await coro if success: result.success += 1 else: result.failed += 1 result.errors.append(msg) # Progress reporting done = result.success + result.failed print(f"\rProgress: {done}/{result.total}", end="", flush=True) print() # Newline after progress return result if __name__ == "__main__": result = asyncio.run(batch_process("./input", pattern="*.txt", max_concurrent=8)) print(f"Done: {result.success} succeeded, {result.failed} failed") for err in result.errors: print(f" ERROR: {err}") ``` ### Task C: Batch processing with error recovery For long-running jobs, track progress and allow resuming: ```python import json from pathlib import Path PROGRESS_FILE = ".batch_progress.json" def load_progress() -> set: if Path(PROGRESS_FILE).exists(): return set(json.loads(Path(PROGRESS_FILE).read_text())) return set() def save_progress(completed: set): Path(PROGRESS_FILE).write_text(json.dumps(list(completed))) def batch_with_resume(input_dir: str, pattern: str = "*.*"): completed = load_progress() files = [f for f in Path(input_dir).glob(pattern) if str(f) not in completed] print(f"Resuming: {len(completed)} done, {len(files)} remaining") for i, filepath in enumerate(files): try: process_single_file(filepath) # your own function: convert, extract, upload... completed.add(str(filepath)) if i % 10 == 0: # Checkpoint every 10 files save_progress(completed) except KeyboardInterrupt: save_progress(completed) print(f"\nSaved progress at {len(completed)} files") raise except Exception as e: print(f"Error on {filepath}: {e}") save_progress(completed) Path(PROGRESS_FILE).unlink() # Clean up on completion ``` ### Task D: Shell-based batch with logging ```bash #!/bin/bash INPUT_DIR="$1" OUTPUT_DIR="$2" LOG_FILE="batch_$(date +%Y%m%d_%H%M%S).log" PARALLEL_JOBS=8 mkdir -p "$OUTPUT_DIR" process_file() { local file="$1" local outfile="$OUTPUT_DIR/$(basename "$file")" # Replace cp with your processing command. Its exit status becomes the job's # status, which --joblog records in the Exitval column. cp "$file" "$outfile" } export -f process_file export OUTPUT_DIR find "$INPUT_DIR" -type f -print0 | parallel -0 --jobs "$PARALLEL_JOBS" --bar \ --joblog "$LOG_FILE" process_file {} echo "Results logged to $LOG_FILE" # Joblog columns: Seq Host Starttime JobRuntime Send Receive Exitval Signal Command awk -F'\t' 'NR>1 {if($7!=0) fail++; else ok++} END {print ok+0" succeeded, "fail+0" failed"}' "$LOG_FILE" # Re-run only the failed jobs later with: parallel --retry-failed --joblog "$LOG_FILE" ``` ## Examples ### Example 1: Convert a directory of Markdown files to PDF **User request:** "Convert all 200 Markdown files in docs/ to PDF" ```bash # Needs pandoc and a LaTeX engine (xelatex) installed find ./docs -name "*.md" -print0 | parallel -0 --bar --jobs 6 \ 'pandoc {} -o {.}.pdf --pdf-engine=xelatex' # Result: one .pdf next to each .md; failures are printed to stderr ``` ### Example 2: Extract text from hundreds of images **User request:** "OCR all scanned documents in the scans/ folder" ```bash # Using tesseract with parallel processing # Writes scan-001.txt next to scan-001.png; use a modest --jobs because tesseract is already multi-threaded find ./scans \( -name "*.png" -o -name "*.jpg" \) -print0 | parallel -0 --bar --jobs 4 \ 'tesseract {} {.} -l eng 2>/dev/null && echo "OK: {}"' ``` ### Example 3: Bulk resize images for web **User request:** "Resize all product images to 800px wide, keep aspect ratio" ```bash mkdir -p ./resized find ./products -name "*.jpg" -print0 | xargs -0 -P 8 -I {} bash -c \ 'magick "$1" -resize 800x -quality 85 "./resized/$(basename "$1")"' _ {} echo "Resized $(ls ./resized | wc -l) images" # Output: Resized 312 images ``` ## Guidelines - Always test batch operations on a small subset (5-10 files) before processing the full set. - Set a reasonable concurrency limit. Start with CPU core count for CPU-bound tasks, or 2-4x for I/O-bound tasks. - Implement progress reporting so users can monitor long-running jobs. - Write errors to a log file rather than stopping the entire batch. - Create a checkpoint/resume mechanism for batches over 100 files. - Back up original files or write output to a separate directory; never overwrite in place without confirmation. - Preview before executing: `parallel --dry-run ...` prints the commands without running them; add the same flag to your own scripts. - Never build shell commands from file names without quoting; use `-print0`/`-0` so names with spaces or quotes cannot break or inject commands. - Monitor system resources (RAM, disk space) during large batch operations. --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/batch-processor/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/batch-processor/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Content & Media Roles: Data Analyst, Data Scientist, Backend Developer, DevOps / SRE, Technical Writer Specialties: Backend, API Tools: Imagemagick Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 7ff035ab4e5e6ed489c3e75dbe29daebc8f8837bc4704651c236222f6d0e4393